48 lines
1.7 KiB
48 lines
1.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import io, os
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import random
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import numpy as np
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from paddle.trainer.PyDataProvider2 import *
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def initHook(settings, height, width, color, num_class, **kwargs):
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settings.height = height
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settings.width = width
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settings.color = color
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settings.num_class = num_class
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if settings.color:
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settings.data_size = settings.height * settings.width * 3
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else:
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settings.data_size = settings.height * settings.width
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settings.is_infer = kwargs.get('is_infer', False)
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settings.num_samples = kwargs.get('num_samples', 2560)
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if settings.is_infer:
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settings.slots = [dense_vector(settings.data_size)]
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else:
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settings.slots = [dense_vector(settings.data_size), integer_value(1)]
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@provider(
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init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
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def process(settings, file_list):
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for i in xrange(settings.num_samples):
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img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
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if settings.is_infer:
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yield img.astype('float32')
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else:
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lab = random.randint(0, settings.num_class - 1)
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yield img.astype('float32'), int(lab)
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